Next Generation Histology-Directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy.
Nathan Heath PattersonMichael TuckAdam LewisAlexis KaushanskyJeremy L NorrisRaf Van de PlasRichard Micheal CaprioliPublished in: Analytical chemistry (2018)
Histology-directed imaging mass spectrometry (IMS) is a spatially targeted IMS acquisition method informed by expert annotation that provides rapid molecular characterization of select tissue structures. The expert annotations are usually determined on digital whole slide images of histological stains where the staining preparation is incompatible with optimal IMS preparation, necessitating serial sections: one for annotation, one for IMS. Registration is then used to align staining annotations onto the IMS tissue section. Herein, we report a next-generation histology-directed platform implementing IMS-compatible autofluorescence (AF) microscopy taken prior to any staining or IMS. The platform enables two histology-directed workflows, one that improves the registration process between two separate tissue sections using automated, computational monomodal AF-to-AF microscopy image registration, and a registration-free approach that utilizes AF directly to identify ROIs and acquire IMS on the same section. The registration approach is fully automated and delivers state of the art accuracy in histology-directed workflows for transfer of annotations (∼3-10 μm based on 4 organs from 2 species) while the direct AF approach is registration-free, allowing targeting of the finest structures visible by AF microscopy. We demonstrate the platform in biologically relevant case studies of liver stage malaria and human kidney disease with spatially targeted acquisition of sparsely distributed (composing less than one tenth of 1% of the tissue section area) malaria infected mouse hepatocytes and glomeruli in the human kidney case study.
Keyphrases
- high resolution
- high throughput
- atrial fibrillation
- mass spectrometry
- deep learning
- single molecule
- endothelial cells
- optical coherence tomography
- high speed
- cancer therapy
- machine learning
- liquid chromatography
- label free
- induced pluripotent stem cells
- drug delivery
- flow cytometry
- single cell
- convolutional neural network
- molecularly imprinted
- tandem mass spectrometry